
import os
import pathlib

# set plotly express to white theme
from dotenv import load_dotenv
from sqlalchemy import create_engine
import matplotlib.pyplot as plt

%matplotlib inline

load_dotenv("../.env")

USE_POSTGRES = os.environ.get('DATABASE_USERNAME',False)
# USE_POSTGRES = False

root_data_dir = pathlib.Path("..").resolve() / "data"
ext_data_dir = root_data_dir / "external"
geopackage = root_data_dir / "aped.gpkg"
if USE_POSTGRES:
  engine = create_engine(
      f"postgresql+psycopg://{os.environ.get('DATABASE_USERNAME')}:{os.environ.get('DATABASE_PASSWORD')}@localhost:5432/{os.environ.get('DATABASE_NAME')}")
else:
  import sqlite3
  engine = sqlite3.connect(geopackage)


import pandas as pd
members = pd.read_sql("SELECT * from member_aph_47", engine)
members


party_counts = members.groupby(["party", "Gender"]).size().reset_index(name='count')
party_counts["%"] = (party_counts["count"] / party_counts.groupby("party")["count"].transform("sum")) * 100
party_counts


# create a cut of members by age
from datetime import date

today = date.today()
members["age"] = pd.to_datetime(members["dob"]).apply(
    lambda x: today.year - x.year - ((today.month, today.day) < (x.month, x.day)))
members["age_group"] = pd.cut(members["age"], bins=[18, 21, 30, 40, 50, 60, 70, 80, 90, 159],
                              labels=["18-20", "21-30", "31-40", "41-50", "51-60", "61-70", "71-80", "81-90", "91+"],
                              ordered=False)
age_counts = members.groupby(["Gender", "chamber", "age_group"]).size().reset_index(name='count')
age_counts
# create a plot of members by age_counts
import plotly.express as px

px.bar(age_counts, x="age_group", y="count", color="Gender", barmode="group", facet_col="chamber",
       title="Age breakdown of Australian politicians by chamber")


# two female memebers missing 2 male members 4 male senators
chamber_counts = members.groupby(["Gender", "chamber"]).size().reset_index(name='count')
px.bar(age_counts, x="chamber", y="count", color="Gender", barmode="group", title="Gender breakdown by chamber")


# calculate members age using their date of birth grouped by party
from datetime import date

today = date.today()
members["age"] = pd.to_datetime(members["dob"]).apply(
    lambda x: today.year - x.year - ((today.month, today.day) < (x.month, x.day)))
members.groupby(["party"]).agg({"age": ["mean", "std"]})


import plotly.express as px

fig = px.bar(party_counts, x="party", y="%", color="Gender",
             title="Gender breakdown of Australian politicians by party")
fig.show()


members_education = pd.read_sql("""
SELECT * from member_secondary_school_education_47
""", engine)


members_education["al_school_sector"].value_counts()

al_school_sector
Government     145
Independent     97
Catholic        67
Name: count, dtype: int64


school_sector_counts = members_education.drop_duplicates(["member", "school_sector"]).groupby(
    ["party", "school_sector"]).size().reset_index(name='count')
school_sector_counts


# create a plot of members school sector type by party

school_sector_counts = members_education.drop_duplicates(["member", "school_sector"]).groupby(
    ["school_sector", "party", ]).size().reset_index(name='count')
fig = px.bar(school_sector_counts, x="party", y="count", color="school_sector", barmode="group",
             title="School sector breakdown of Australian politicians by party")
fig.show()


school_sector_counts = members_education.drop_duplicates(["member", "school_sector"]).groupby(
    ["party", "school_sector"]).size().reset_index(name='count')
fig = px.bar(school_sector_counts, x="school_sector", y="count", color="party",
             title="School sector breakdown of Australian politicians by party")
fig.show()


population_school_sector = {
    "Catholic": 19.7,
    "Government": 64.5,
    "Independent": 15.8643437545764}
pop_school_sector = pd.DataFrame.from_dict(population_school_sector, orient="index").reset_index()
pop_school_sector["group"] = "Australian Population"
pop_school_sector.columns = ["school_sector", "%", "group"]
pop_school_sector


# create a plot of members as a percentage school sector

school_sector_counts = members_education.drop_duplicates(["member", "al_school_sector"]).groupby(
    ["al_school_sector"]).size().reset_index(name='count')
school_sector_counts.rename(columns={"al_school_sector": "school_sector"}, inplace=True)
school_sector_counts["%"] = (school_sector_counts["count"] / school_sector_counts["count"].sum()) * 100
school_sector_counts["group"] = "Australian Politicians"

fig = px.bar(pd.concat([school_sector_counts, pop_school_sector]), x="school_sector", y="%",
             color="group",
             barmode="group", title="School sector breakdown of Australian politicians")
fig.show()


# create a plot of members as a percentage school sector depending on if is_representative or senate
school_sector_counts = members_education.drop_duplicates(["member", "al_school_sector"]).groupby(
    ["chamber", "al_school_sector"]).size().reset_index(name='count')
school_sector_counts["%"] = (school_sector_counts["count"] / school_sector_counts.groupby("chamber")[
    "count"].transform("sum")) * 100
fig = px.bar(school_sector_counts, x="chamber", y="%", color="al_school_sector",
             title="School sector breakdown of Australian politicians by party", labels={"chamber": "Chamber"})
fig.show()


# lets extract out our acara_financial data and see which school sectors are in our data
import geopandas as gpd

load_dotenv("../.env")

if USE_POSTGRES:
  gdf = gpd.read_postgis(
      'SELECT m.*, "total enrolments" as total_students FROM member_secondary_school_education_47 m LEFT JOIN acara_school_profile_2022 a on m.acara_id = a."acara sml id"::int',
      engine, geom_col='geom')
else:
  gdf_47 = gpd.read_file(geopackage, layer="member_secondary_school_education_47")
  gdf_47 = gdf_47.convert_dtypes()
  asp_47 = gpd.read_file(geopackage, layer="acara_school_profile_2022")[["acara sml id", "total enrolments"]]
  asp_47 = asp_47.convert_dtypes()
  gdf = pd.merge(gdf_47, asp_47, left_on="acara_id", right_on="acara sml id", how="left")
  gdf.rename(columns={"total enrolments": "total_students"}, inplace=True)
gdf = gdf.convert_dtypes()
# fill missing students with min 50 students
gdf["total_students"] = gdf["total_students"].fillna('50').apply(lambda x: '50' if not x else x)
gdf["total_students"] = gdf["total_students"].astype(int)

# Group by party_abbrev and school_sector and avg total government funding
gdf["australian_government_recurrent_funding_per_student"] = gdf[
    "australian_government_recurrent_funding_per_student"].fillna(0).astype(int)
gdf["state__territory_government_recurring_funding_per_student"] = gdf[
    "state__territory_government_recurring_funding_per_student"].fillna(0).astype(int)
gdf["other_private_sources_per_student"] = gdf["other_private_sources_per_student"].fillna(0).astype(int)
gdf["total_gross_income_per_student"] = gdf["total_gross_income_per_student"].fillna(0).astype(int)


grouped = gdf.groupby(["party_abbrv", "school_sector"]).agg(
    {"other_private_sources_per_student": "mean", "total_gross_income_per_student": "mean"}).reset_index()
px.bar(grouped, x="party_abbrv", y="other_private_sources_per_student", color="school_sector", barmode="group")


px.bar(grouped, x="party_abbrv", y="total_gross_income_per_student", color="school_sector", barmode="group")


# Select most common secondary school from members_secondary_school APH data
most_common_schools = pd.read_sql(
    "SELECT secondaryschool,count(*) as members_attending  FROM members_secondary_school WHERE secondaryschool != '' and secondaryschool is not null GROUP BY secondaryschool ORDER BY count(*) desc limit 10",
    engine)
most_common_schools


# select those who attended the same school get value counts of name and show members who attended the same school
school_counts = members_education["name"].value_counts()
pd.merge(members_education, school_counts[school_counts > 1].reset_index(), left_on="name", right_on="name")[
    ["name", "member", "party", "school_sector", "count"]].sort_values(["name", "member"])


# select members who don't have a high school in education data
pd.read_sql(
    "SELECT * FROM members WHERE id not in (SELECT member_id FROM member_education JOIN education e on member_education.education_id = e.id WHERE e.is_high_school = TRUE ) AND (high_school is null or high_school != 'International' ) ",
    engine)


# Do percentage change in public to non-goverment school from parliamnent 46 to 47
ph_46 = pd.read_sql("SELECT * FROM member_secondary_school_education_46", engine)
ph_47 = pd.read_sql("SELECT * FROM member_secondary_school_education_47", engine)


school_sector_counts_46 = ph_46.drop_duplicates(["member", "school_sector"]).groupby(
    ["party", "school_sector"]).size().reset_index(name='count')
school_sector_counts_46["%"] = (school_sector_counts_46["count"] / school_sector_counts_46.groupby("party")[
    "count"].transform("sum")) * 100
school_sector_counts_46["parliament"] = "46th Parliament"

school_sector_counts_47 = ph_47.drop_duplicates(["member", "school_sector"]).groupby(
    ["party", "school_sector"]).size().reset_index(name='count')
school_sector_counts_47["%"] = (school_sector_counts_47["count"] / school_sector_counts_47.groupby("party")[
    "count"].transform("sum")) * 100
school_sector_counts_46["parliament"] = "46th Parliament"
school_sector_counts_47["parliament"] = "47th Parliament"
# calculate differences between school_sector_counts_46 and school_sector_counts
pd.concat([school_sector_counts_46, school_sector_counts_47]).sort_values(["party", "school_sector"])


school_sector_counts_prev = ph_46.drop_duplicates(["member", "al_school_sector"]).groupby(
    ["al_school_sector"]).size().reset_index(name='count')
school_sector_counts_prev.rename(columns={"al_school_sector": "school_sector"}, inplace=True)
school_sector_counts_prev["%"] = (school_sector_counts_prev["count"] / school_sector_counts_prev["count"].sum()) * 100
school_sector_counts_prev["group"] = "Australian Politicians 46th Parliament"

school_sector_counts = ph_47.drop_duplicates(["member", "al_school_sector"]).groupby(
    ["al_school_sector"]).size().reset_index(name='count')
school_sector_counts.rename(columns={"al_school_sector": "school_sector"}, inplace=True)
school_sector_counts["%"] = (school_sector_counts["count"] / school_sector_counts["count"].sum()) * 100
school_sector_counts["group"] = "Australian Politicians 47th Parliament"

fig = px.bar(pd.concat([school_sector_counts, school_sector_counts_prev, pop_school_sector]), x="school_sector", y="%",
             color="group",
             barmode="group", title="School sector breakdown of Australian politicians past 2 parliaments")
fig.show()


from pywaffle import Waffle
import matplotlib.pyplot as plt
# slightly different to SMH as they set those who did not graduate or who attended overseas to other
members_46 = pd.read_sql("SELECT * FROM member_aph_46", engine)
members_46_high_school = members_46.groupby("high_school").size().reset_index(name='count')
members_46_high_school["%"] = (members_46_high_school["count"] / members_46_high_school["count"].sum()) * 100
members_46_high_school

	id	member_id	member	party	party_abbrev	district	is_senator	is_representative	mp_id	start	...	RepresentedMinistries	RepresentedShadowMinistries	ParliamentaryPositions	Honours	Occupations	SecondaryOccupations	Qualifications	ElectorateService	PartyParliamentaryService	PartyCommitteeService
0	1	128	Eric Abetz	Coalition	LNP	Tasmania	True	False	N26	1994-02-22	...	[Parliamentary Secretary, Special Minister of ...	[Shadow Minister, Shadow Minister, Shadow Mini...	[]	{}	[Barrister and solicitor]	[Barrister, Legal, Social and Welfare Professi...	[Bachelor of Arts, University of Tasmania, Bac...	[]	[{'RoSId': 26985, 'RoSType': 'Parliamentary Se...	[{'RoSId': 943, 'RoSType': 'Committee Service'...
1	2	182	Mary Doyle	Australian Labor Party	ALP	Aston	False	True	299962	2023-04-01	...	[]	[]	[]	{}	[]	[]	[]	[]	[]	[]
2	3	54	Anthony Albanese	Australian Labor Party	ALP	Grayndler	False	True	R36	1996-03-02	...	[Cabinet Minister, Cabinet Minister, Cabinet M...	[Shadow Parliamentary Secretary, Shadow Parlia...	[]	{}	[Senior Policy Adviser to the Premier, the Hon...	[Credit or Loans Officer, Numerical Clerks, Cl...	[Bachelor of Economics, University of Sydney]	[{'Electorate': 'Grayndler', 'State': 'New Sou...	[{'RoSId': 27149, 'RoSType': 'Parliamentary Se...	[{'RoSId': 1127, 'RoSType': 'Committee Service...
3	4	187	John Alexander	Coalition	LNP	Bennelong	False	True	M3M	2010-08-21	...	[]	[]	[]	{"Australian Sports Medal, 2000","Awarded a Me...	[Federation Cup Captain/Coach, 2005., Sports c...	[Tennis Coach, Sportspersons nec, Chief Execut...	[]	[{'Electorate': 'Bennelong', 'State': 'New Sou...	[{'RoSId': 27156, 'RoSType': 'Parliamentary Se...	[{'RoSId': 1133, 'RoSType': 'Committee Service...
4	5	52	Katie Allen	Coalition	LNP	Higgins	False	True	282986	2019-05-18	...	[]	[]	[]	{"Fellow of the Australian Academy of Health a...	[Scientific Advisory Board Member at Before Br...	[Life Scientists nec, Research and Development...	[Doctor of Philosophy, University of Melbourne...	[{'Electorate': 'Higgins', 'State': 'Victoria'...	[{'RoSId': 54212, 'RoSType': 'Parliamentary Se...	[{'RoSId': 54504, 'RoSType': 'Committee Servic...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
278	279	205	Jason Wood	Coalition	LNP	La Trobe	False	True	E0F	2013-09-07	...	[Assistant Minister]	[Shadow Parliamentary Secretary, Shadow Parlia...	[]	{"Awarded a Centenary Medal, 01.01.2001","Quee...	[Senior Sergeant in the Counter Terrorism Coor...	[Police Officer, Detective, Protective Service...	[Graduate Diploma Innovation Service Managemen...	[{'Electorate': 'La Trobe', 'State': 'Victoria...	[{'RoSId': 27705, 'RoSType': 'Parliamentary Se...	[{'RoSId': 2472, 'RoSType': 'Committee Service...
279	280	265	Ken Wyatt	Coalition	LNP	Hasluck	False	True	M3A	2010-08-21	...	[Assistant Minister, Assistant Minister, Assis...	[]	[]	{"Indigenous Education Warrior Award, World In...	[Director for Aboriginal Health at the Departm...	[Management Consultant, Policy and Planning Ma...	[Bachelor of Education, Churchlands College of...	[{'Electorate': 'Hasluck', 'State': 'Western A...	[{'RoSId': 27710, 'RoSType': 'Parliamentary Se...	[{'RoSId': 2479, 'RoSType': 'Committee Service...
280	281	242	Terry Young	Coalition	LNP	Longman	False	True	201906	2019-05-18	...	[]	[]	[Member of the Speaker's Panel]	{}	[Franchisee, Drummond Golf, Maroochydore from ...	[Other Factory Process Workers nfd, Service St...	[]	[{'Electorate': 'Longman', 'State': 'Queenslan...	[{'RoSId': 54008, 'RoSType': 'Parliamentary Se...	[{'RoSId': 54534, 'RoSType': 'Committee Servic...
281	282	230	Tony Zappia	Australian Labor Party	ALP	Makin	False	True	HWB	2007-11-24	...	[]	[Shadow Parliamentary Secretary, Shadow Parlia...	[]	{}	[Fitness centre manager from 1981 to 2007., Re...	[Bank Worker, Fitness Centre Manager, Numerica...	[]	[{'Electorate': 'Makin', 'State': 'South Austr...	[{'RoSId': 27719, 'RoSType': 'Parliamentary Se...	[{'RoSId': 2501, 'RoSType': 'Committee Service...
282	283	63	Trent Zimmerman	Coalition	LNP	North Sydney	False	True	203092	2015-12-05	...	[]	[]	[Member of the Speaker's Panel]	{}	[Deputy Chief Executive Officer and Director o...	[Information and Organisation Professionals ne...	[]	[{'Electorate': 'North Sydney', 'State': 'New ...	[{'RoSId': 27723, 'RoSType': 'Parliamentary Se...	[{'RoSId': 2508, 'RoSType': 'Committee Service...

	party	Gender	count	%
0	Australian Greens	Female	10	55.555556
1	Australian Greens	Male	8	44.444444
2	Australian Labor Party	Female	60	51.282051
3	Australian Labor Party	Male	57	48.717949
4	Centre Alliance	Female	1	50.000000
5	Centre Alliance	Male	1	50.000000
6	Coalition	Female	33	26.400000
7	Coalition	Male	92	73.600000
8	Independent	Female	9	69.230769
9	Independent	Male	4	30.769231
10	Jacqui Lambie Network	Female	2	100.000000
11	Katter's Australian Party	Male	1	100.000000
12	Liberal Democratic Party	Female	1	100.000000
13	Pauline Hanson's One Nation	Female	1	50.000000
14	Pauline Hanson's One Nation	Male	1	50.000000
15	United Australia Party	Male	1	100.000000
16	United Australia Party [2018]	Male	1	100.000000

	age
	mean	std
party
Australian Greens	50.333333	11.812257
Australian Labor Party	52.743590	9.897016
Centre Alliance	57.500000	10.606602
Coalition	52.983871	8.529227
Independent	51.692308	6.848170
Jacqui Lambie Network	52.000000	0.000000
Katter's Australian Party	77.000000	NaN
Liberal Democratic Party	55.000000	NaN
Pauline Hanson's One Nation	67.500000	0.707107
United Australia Party	39.000000	NaN
United Australia Party [2018]	59.000000	NaN

	party	school_sector	count
0	Australian Greens	Non-government	6
1	Australian Greens	Public	12
2	Australian Labor Party	Non-government	60
3	Australian Labor Party	Public	52
4	Centre Alliance	Non-government	2
5	Centre Alliance	Public	1
6	Coalition	Non-government	67
7	Coalition	Public	60
8	Independent	Non-government	9
9	Independent	Public	5
10	Jacqui Lambie Network	Non-government	1
11	Jacqui Lambie Network	Public	1
12	Liberal Democratic Party	Non-government	1
13	Pauline Hanson's One Nation	Non-government	1
14	Pauline Hanson's One Nation	Public	1
15	United Australia Party	Non-government	1
16	United Australia Party [2018]	Non-government	1

	secondaryschool	members_attending
0	Scotch College	5
1	St Ignatius College	4
2	St Peter's College	4
3	Wesley College	4
4	Marist College	3
5	Geelong Grammar School	3
6	Norwood High School	2
7	Aquinas College	2
8	Christian Brothers' College	2
9	Scotch College Melbourne	2

47th Australian Parliament basic statistics¶

Educational school sector split of politicians¶

Members attending same school¶

	school_sector	%	group
0	Catholic	19.700000	Australian Population
1	Government	64.500000	Australian Population
2	Independent	15.864344	Australian Population

	name	member	party	school_sector	count
78	Alfred Deakin High School	Andrew Laming	Coalition	Public	2
77	Alfred Deakin High School	Phillip Thompson	Coalition	Public	2
47	Anglican Church Grammar School	Glenn Sterle	Australian Labor Party	Non-government	2
46	Anglican Church Grammar School	Matt Thistlethwaite	Australian Labor Party	Non-government	2
29	Aquinas College	Celia Hammond	Coalition	Non-government	2
...	...	...	...	...	...
33	Wesley College, Victoria	Shayne Neumann	Australian Labor Party	Non-government	2
8	Woonona High School	Karen Grogan	Australian Labor Party	Public	2
9	Woonona High School	Mary Doyle	Australian Labor Party	Public	2
20	Xavier College	David Pocock	Independent	Non-government	2
21	Xavier College	Katie Allen	Coalition	Non-government	2

	id	orig_id	orig_table	member	district	is_senator	is_representative	graduated	start	wiki_link	dob	mp_id	party_id	chamber	high_school	preferred_name	aph_name
0	86	3	47	Alex Antic	South Australia	True	False	True	2019-07-01	http://www.wikidata.org/entity/Q63520981	1974-12-22	269375	42	senate	Public	Alex Antic	Alexander Antic
1	122	12	46	Vince Connelly	Stirling	False	True	True	2019-05-18	http://www.wikidata.org/entity/Q64585316	1978-09-16	282984	42	house	Non-government	Vince Connelly	Vincent Connelly
2	90	106	47	Karen Grogan	South Australia	True	False	True	2021-09-21	http://www.wikidata.org/entity/Q108617920	1960-01-01	296331	11	senate	None	None	Karen Grogan
3	243	21	46	Nicolle Flint	Boothby	False	True	True	2016-07-02	http://www.wikidata.org/entity/Q25756083	1978-07-15	245550	42	house	Non-government	None	Nicolle Flint
4	75	151	47	Michelle Ananda-Rajah	Higgins	False	True	True	2022-05-21	http://www.wikidata.org/entity/Q90398588	1972-12-10	290544	11	house	None	None	Michelle Ananda-Rajah
5	142	188	47	Sam Birrell	Nicholls	False	True	True	2022-05-21	http://www.wikidata.org/entity/Q112131083	1975-01-01	288713	42	house	None	None	Sam Birrell
6	99	155	47	Milton Dick	Oxley	False	True	True	2016-07-02	http://www.wikidata.org/entity/Q20983429	1972-07-21	53517	11	house	Non-government	Milton Dick	Dugald Dick
7	93	173	47	Peter Khalil	Wills	False	True	True	2016-07-02	http://www.wikidata.org/entity/Q25756187	1973-03-23	101351	11	house	Non-government	None	Peter Khalil
8	251	182	47	Rob Mitchell	McEwen	False	True	True	2010-08-21	http://www.wikidata.org/entity/Q7340383	1967-09-09	M3E	11	house	Public	Rob Mitchell	Robert Mitchell
9	152	123	47	Llew O'Brien	Wide Bay	False	True	False	2016-07-02	http://www.wikidata.org/entity/Q25756234	1972-06-26	265991	42	house	None	Llew O'Brien	Llewellyn O'Brien
10	57	75	47	Graham Perrett	Moreton	False	True	True	2007-11-24	http://www.wikidata.org/entity/Q5593142	1966-01-05	HVP	11	house	Public	None	Graham Perrett
11	23	200	47	Stephen Bates	Brisbane	False	True	True	2022-05-21	http://www.wikidata.org/entity/Q112150148	1992-11-23	300246	12	house	None	None	Stephen Bates
12	175	219	47	Tracey Roberts	Pearce	False	True	True	2022-05-21	http://www.wikidata.org/entity/Q109850050	1960-01-01	157125	11	house	None	None	Tracey Roberts
13	103	48	46	Julian Simmonds	Ryan	False	True	True	2019-05-18	http://www.wikidata.org/entity/Q64226194	1985-08-29	282983	42	house	Non-government	None	Julian Simmonds
14	29	49	46	Arthur Sinodinos	New South Wales	True	False	True	2011-10-13	http://www.wikidata.org/entity/Q4800293	1957-02-25	BV7	42	senate	Public	None	Arthur Sinodinos
15	160	132	47	Marielle Smith	South Australia	True	False	True	2019-07-01	http://www.wikidata.org/entity/Q63531157	1986-12-30	281603	11	senate	Both	None	Marielle Smith
16	207	98	47	Jordon Steele-John	Western Australia	True	False	True	2017-11-10	http://www.wikidata.org/entity/Q33139188	1994-10-14	250156	12	senate	Home-Schooled	None	Jordon Steele-John
17	13	86	47	Jana Stewart	Victoria	True	False	True	2022-04-06	http://www.wikidata.org/entity/Q111513180	1987-01-01	299352	11	senate	None	None	Jana Stewart
18	222	206	47	Susan Templeman	Macquarie	False	True	True	2016-07-02	http://www.wikidata.org/entity/Q25753992	1963-07-30	181810	11	house	Public	None	Susan Templeman
19	67	94	47	Jess Walsh	Victoria	True	False	True	2019-07-01	http://www.wikidata.org/entity/Q64711059	1971-05-16	252157	11	senate	Non-government	None	Jess Walsh
20	82	174	47	Peter Whish-Wilson	Tasmania	True	False	True	2012-06-20	http://www.wikidata.org/entity/Q7177677	1968-02-24	195565	12	senate	Both	None	Peter Whish-Wilson

	party	school_sector	count	%	parliament
0	Australian Greens	Non-government	9	75.000000	46th Parliament
0	Australian Greens	Non-government	6	33.333333	47th Parliament
1	Australian Greens	Public	3	25.000000	46th Parliament
1	Australian Greens	Public	12	66.666667	47th Parliament
2	Australian Labor Party	Non-government	48	49.484536	46th Parliament
2	Australian Labor Party	Non-government	60	53.571429	47th Parliament
3	Australian Labor Party	Public	49	50.515464	46th Parliament
3	Australian Labor Party	Public	52	46.428571	47th Parliament
4	Centre Alliance	Non-government	1	50.000000	46th Parliament
4	Centre Alliance	Non-government	2	66.666667	47th Parliament
5	Centre Alliance	Public	1	50.000000	46th Parliament
5	Centre Alliance	Public	1	33.333333	47th Parliament
6	Coalition	Non-government	63	56.250000	46th Parliament
6	Coalition	Non-government	67	52.755906	47th Parliament
7	Coalition	Public	49	43.750000	46th Parliament
7	Coalition	Public	60	47.244094	47th Parliament
8	Independent	Non-government	4	66.666667	46th Parliament
8	Independent	Non-government	9	64.285714	47th Parliament
9	Independent	Public	2	33.333333	46th Parliament
9	Independent	Public	5	35.714286	47th Parliament
10	Jacqui Lambie Network	Non-government	1	50.000000	47th Parliament
10	Jacqui Lambie Network	Public	1	100.000000	46th Parliament
11	Jacqui Lambie Network	Public	1	50.000000	47th Parliament
11	Katter's Australian Party	Public	1	100.000000	46th Parliament
12	Liberal Democratic Party	Non-government	1	100.000000	46th Parliament
12	Liberal Democratic Party	Non-government	1	100.000000	47th Parliament
13	Pauline Hanson's One Nation	Non-government	1	50.000000	47th Parliament
13	Pauline Hanson's One Nation	Public	1	100.000000	46th Parliament
14	Pauline Hanson's One Nation	Public	1	50.000000	47th Parliament
15	United Australia Party	Non-government	1	100.000000	47th Parliament
16	United Australia Party [2018]	Non-government	1	100.000000	47th Parliament

	high_school	count	%
0	Both	17	7.327586
1	Home-Schooled	1	0.431034
2	International	2	0.862069
3	Non-government	114	49.137931
4	Public	98	42.241379